Hi,
I’m trying to help a colleague find freely available task fMRI data that has a fast TR, ideally lower than 1.0 and ideally a range of TRs. The recently released NARPS data is not bad, TR=1s, but it is just the one TR and the design is involved.
There are certainly studies out there that examine the impact of TR on task fMRI – eg. Sahib et al. 2018 but they don’t share the data.
Does anyone have pointers to some data?
Thanks in advance!
-Tom
Ahoi hoi @nicholst,
unfortunately I don’t have any direct pointers to such datasets, but I think there
are two ways you could explore:
datalad searchdatalads search functionality to get an initial & rather broad overview of what’s out there in the datasets that integrate with datalad. Here’s an example for searching for functional data with a TR of 1 using datalad version 0.11.8:#install datalad super-dataset datalad install /// cd datalad #search for functional data with a TR of 1 and save into a .txt datalad -c datalad.search.index-egrep-documenttype=all search bids.type:bold bids.RepetitionTime:1 > datasets_short_tr.txt
Unfortunately, the search value needs to be a specific number AFAIK (hence, something like bids.RepetitionTime:<1 is not possible). @yarikoptic, @eknahm, @adina do you have perhaps any insights/ideas here?
bold data, you could query the data following @miykael’s example here.Sorry again for not providing any precise pointers.
HTH, cheers, Peer
Hi @nicholst,
I was in charge of recording a task fMRI dataset with a fast TR of 600ms. We’ve recorded 17 subjects, 6 runs of 600 volumes each (vox.res.=3x3x3; slab through V1 and A1), with 28 events per run (ISI=12s). Tonotopy and Retinotopy were also recorded, with a TR of 600ms as well.
Participant’s consent for publicly sharing the data was acquired and the ethics specified that the data will be shared via openneuro. Unfortunately, I’m not the decider of the time-line and cannot predict when this dataset will be available. But if the interest is there, I’m happy to inquire if a collaboration can be established to facilitate and accelerate access to the data.
On a side note: I personally was also looking for a fast TR dataset last year and went through many public sources. The NARPS dataset was by far the best with a fast TR, a lot of subjects, and a very high quality of data.
Cheers,
Michael
sorry for the delay… couldn’t reply until I at least try to provide a pragmatic answer in a form of this PR: https://github.com/datalad/datalad/pull/3948 which should let you use the python you love to construct your query:
$> datalad -f '{path}: {metadata[bids][RepetitionTime]}' \
-c datalad.search.index-pyeval-documenttype=all \
search -d ~/datalad --mode pyeval \
"(bids['type'] == 'bold') and (bids['RepetitionTime'] < 1)"
/home/yoh/datalad/dbic/QA/sourcedata/sub-emmet/ses-20180521/func/sub-emmet_ses-20180521_task-rest_acq-3mm_bold.dicom.tgz: 0.426
/home/yoh/datalad/dbic/QA/sourcedata/sub-emmet/ses-20180531/func/sub-emmet_ses-20180531_task-rest_acq-3mm_bold.dicom.tgz: 0.426
/home/yoh/datalad/dbic/QA/sub-emmet/ses-20180521/func/sub-emmet_ses-20180521_task-rest_acq-3mm_bold.nii.gz: 0.426
/home/yoh/datalad/dbic/QA/sub-emmet/ses-20180531/func/sub-emmet_ses-20180531_task-rest_acq-3mm_bold.nii.gz: 0.426
/home/yoh/datalad/dbic/QA/sub-qa/ses-20161128/func/sub-qa_ses-20161128_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20161205/func/sub-qa_ses-20161205_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20161212/func/sub-qa_ses-20161212_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20161219/func/sub-qa_ses-20161219_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20170103/func/sub-qa_ses-20170103_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20170109/func/sub-qa_ses-20170109_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20170117/func/sub-qa_ses-20170117_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20170123/func/sub-qa_ses-20170123_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/dbic/QA/sub-qa/ses-20170130/func/sub-qa_ses-20170130_task-rest_acq-p2Xs4X35mm_bold.nii.gz: 0.525
/home/yoh/datalad/openneuro/ds001021/sub-A00008326/ses-DS2/func/sub-A00008326_ses-DS2_task-CHECKERBOARD_acq-645_bold.nii.gz: 0.645
/home/yoh/datalad/openneuro/ds001021/sub-A00008326/ses-DS2/func/sub-A00008326_ses-DS2_task-rest_acq-645_bold.nii.gz: 0.645
/home/yoh/datalad/openneuro/ds001178/sub-beast329/func/sub-beast329_task-rest_bold.nii.gz: 0.68
/home/yoh/datalad/openneuro/ds001568/sub-01/func/sub-01_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-01/func/sub-01_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-01/func/sub-01_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-01/func/sub-01_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-02/func/sub-02_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-02/func/sub-02_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-02/func/sub-02_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-02/func/sub-02_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-03/func/sub-03_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-03/func/sub-03_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-03/func/sub-03_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-03/func/sub-03_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-04/func/sub-04_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-04/func/sub-04_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-04/func/sub-04_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-04/func/sub-04_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-05/func/sub-05_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-05/func/sub-05_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-05/func/sub-05_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-05/func/sub-05_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-06/func/sub-06_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-06/func/sub-06_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-06/func/sub-06_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-06/func/sub-06_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-07/func/sub-07_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-07/func/sub-07_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-07/func/sub-07_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-07/func/sub-07_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-08/func/sub-08_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-08/func/sub-08_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-08/func/sub-08_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-08/func/sub-08_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-09/func/sub-09_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-09/func/sub-09_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-09/func/sub-09_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-09/func/sub-09_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-10/func/sub-10_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-10/func/sub-10_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-10/func/sub-10_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-10/func/sub-10_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-11/func/sub-11_task-rest_acq-moldOFF_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-11/func/sub-11_task-rest_acq-moldOFF_run-2_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-11/func/sub-11_task-rest_acq-moldON_run-1_bold.nii.gz: 0.8
/home/yoh/datalad/openneuro/ds001568/sub-11/func/sub-11_task-rest_acq-moldON_run-2_bold.nii.gz: 0.8
datalad -f '{path}: {metadata[bids][RepetitionTime]}' -c search -d ~/datalad 32.21s user 1.47s system 100% cpu 33.542 total
Hi @yarikoptic,
thanks for the answer. Oh damn, when a question leads to an immediate feature implementation…chances are high you’re on neurostars! Thanks a lot!
Can folks outside the main DataLad universe be of help here?
I guess only by testing and joining me in “constructive whining” on https://github.com/OpenNeuroOrg/datalad-service/issues , but I will push updates at least for some recent datasets to our datasets.datalad.org soon.
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